Addressing the Midlife Mortality Crisis: Place-Based Modeling, Trend Analysis and Policy Interventions

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PROJECT SUMMARY This project will study midlife mortality trends by analyzing the local correlates and risk factors of cause of death in their place-based context between 1990 and 2021. Using cause-specific death data, sociodemo- graphic characteristics, and economic trends, the research will generate trajectories of midlife mortality risks for dynamically-derived spatial units. What makes this project innovative and significant is (1) mortality data at the block, zip code or tract level; (2) methods in spatial analysis, time series trajectory determination, and mul- tiscalar statistical analysis; and (3) an innovative and integrated approach to policy research, conducted by a team experienced in demographic analysis, multiscalar spatial and temporal statistics, and rural health policy formulation. This work responds to research and policy discussions regarding potentially-modifiable attributes of places related to rising mortality among middle-aged adults. Several cause-specific mortality trends warrant attention: rising rates of suicides, cardiometabolic diseases, and deaths due to drug or alcohol abuse, along with their interactions with select infectious diseases (e.g., Hepatitis C and COVID-19). The project has five specific aims: (1) For select U.S. states where block- or tract-level mortality data are available, researchers will create a novel micro-scale database with cause-specific mortality records and contextual data including cen- sus-based demographic data, employment and business data, property characteristics, and crime data. (2) The research makes use of an iterative algorithm to identify clusters of micro-units that have common, cause- specific mortality patterns, using community detection methods, in order to identify the most salient levels of spatial units at which processes affecting midlife mortality occur. It also assesses spatial autocorrelation to identify mortality hot- and cold-spots (spatial) and undertakes extreme event detection to identify hot-moments (temporal). (3) The project uses multi-channel sequencing to classify geographic units, for example, where population has grown or diminished, aged, or seen an increase in children, or where the economy has created wealth. These trajectories constitute a set of place-based classifications for later analysis. (4) The project’s analysis will use spatiotemporal models to estimate the effect of local-to-regional determinants of cause-spe- cific mortality in middle-aged adults, with two strategies: (A) Assess the role of different place-based determi- nants of midlife mortality, in order to evaluate theories of midlife mortality differentials and to test specific hy- potheses (e.g., economic distress). (B) Identify the optimal scale at which determinants with the greatest im- pact operate to inform policy recommendations designed to target midlife mortality for specific causes of death. (5) Designing policy interventions is fundamental to the project, which begins with a policy mapping process, and builds on the results of the multiscalar analysis to design intervention strategies targeting the determinants of mortality differentials and the characteristics of places at greatest risk of continued mortality challenges.